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3,737 results for “plant species”
Rapid root to leaf uptake of inorganic and amino acid nitrogen in three dryland plant species.
Our aim was to quantify inorganic and organic nitrogen (N) uptake and compare short-term nutrient acquisition patterns among three dryland plant species: Bouteloua eriopoda, Achnatherum hymenoides, and Gutierrezia sarothrae collected from a mixed grassland community in the Northern Chihuahuan Desert to better understand how asynchronous resource availability may influence biotic interactions and nutrient retention in these ecosystems. We collected living plants from two locations within the Sevilleta National Wildlife Refuge and transplanted them into pots maintained in the greenhouse with supplemental light and water for two months. We then conducted a greenhouse experiment using these species to compare nutrient uptake of 15N-labeled ammonium (NH4+), nitrate (NO3-), and glutamate (an amino acid) over 12 to 48 hours. Our study examined three main questions: (1) How rapidly do these dryland plants take up available soil N?, (2) Does leaf uptake differ among inorganic and amino acid N forms?, and (3) Do plant species differ in the speed or form of short-term N uptake?. In the greenhouse, we applied one of three isotopic 15N tracers directly to plant roots and quantified N uptake and recovery in leaves after 12, 24, and 48 hours. We found that plants took up inorganic and amino acid N to leaves as rapidly as 12 h following application, and N uptake more than doubled between 24 and 48 h. Inorganic N uptake was 3-4x higher than organic N uptake in all three species, and plants took up ammonium and nitrate at 2-3x faster rates than glutamate. On average, B. eriopoda had higher inorganic N recovery and uptake speeds, while G. sarothrae had the highest organic N uptake over time. A. hymenoides root to leaf uptake was ~50% lower than the other two species after 48 h. Plants showed similar patterns of short-term foliar uptake and recovery indicating a lack of niche partitioning by N form among the three dryland species measured. Our results suggest that soil inorganic N, par
Biomass and Plant Species Composition on Hog and Metompkin Islands, 2020-2022
Beginning in 2020, annual plant surveys were conducted along 3 cross-shore transects on Hog and Metompkin Islands along the Atlantic coast of Virginia. Transects were established starting at the high water mark, and continuing inland until reaching a shrub thicket. Plots were placed 10m apart in beach and swale habitats, and 5m apart across dunes. At each plot, percent composition was measured within a 0.25m^2 quadrat. Aboveground biomass was also obtained within a randomly placed 10x100cm^2 quadrat. Biomass was then dried and weighed. Due to logistical constraints, biomass was not collected in 2022.
Modifications of the plant-pollinator network structure and species' roles along a gradient of urbanization
<p>This file includes data and codes used in the article titled: " Modifications of the plant-pollinator network structure and species’ roles along a gradient of urbanization".</p> <p>Data include plant-pollinator interactions sampled in each site (1-12) at each sampling event (6 events) in the three urbanization classes (low, medium, high). Each row is a single insect pollinator X plant interaction. Full species names and abbreviations used in figures in the Supplementary Information are reported.<br> The data file is .txt with tab-separated values.</p>
Eyebright species maintenance (scripts and data accompanying Becher et al., Plant Communications)
<p><strong>This gzipped TAR ball contains data and scripts related to the study on Fair Isle eyebrights by Hannes Becher, Max R. Brown, Gavin Powell, Chris Metherell, Nick J. Riddiford, and Alex D. Twyford, submitted to Plant Communications.</strong></p> <p>Data: genome assembly of<em> Euphrasia arctica</em>, variant call files of the "tetraploid" and "conserved" sets of scaffolds, per-individual k-mer spectra, mapping depths, etc.</p> <p>Scripts: R scripts for the analysis of plant trait data, heterozygosity, ect.; an ipython notebook for the analysis of variant data, and a Mathematica notebook with the derivation of the formulae used to fit pop gen parameters to k-mer spectra.</p>
Country Compendium of the Global Register of Introduced and Invasive Species: Standardization to Records in World Flora Online or the World Checklist of Vascular Plants
<p>The <strong>Country Compendium of the Global Register of Introduced and Invasive Species (GRIIS)</strong> is a collation of data across 196 individual country checklists of alien species, along with a designation of those species associated with evidence of impact at a country level. This compendium is available via <a href="https://zenodo.org/records/6348164">Zenodo</a> and was described by Pagad et al. <a href="https://www.nature.com/articles/s41597-022-01514-z">2022</a>:</p><ul><li>Shyama Pagad, Stewart Bisset, & Melodie A. McGeoch. (2022). Country Compendium of the Global Register of Introduced and Invasive Species. Dataset. (V1_0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6348164">https://doi.org/10.5281/zenodo.6348164</a></li><li>Pagad, S., Bisset, S., Genovesi, P. <i>et al.</i> Country Compendium of the Global Register of Introduced and Invasive Species. <i>Sci Data</i> <strong>9</strong>, 391 (2022). <a href="https://doi.org/10.1038/s41597-022-01514-z">https://doi.org/10.1038/s41597-022-01514-z</a></li></ul><p> </p><p>Here I provide direct and fuzzy matches for species listed for the Plantae Kingdom in GRIIS with accepted plant names in <strong>World Flora Online</strong> (<a href="https://www.worldfloraonline.org/downloadData">version 2023.03</a>; Borsch et al. <a href="https://doi.org/10.1002/tax.12373">2020</a>) or the <strong>World Checklist of Vascular Plants</strong> (<a href="https://doi.org/10.34885/nswv-8994">version 10</a>; Govaerts et al. <a href="https://www.nature.com/articles/s41597-021-00997-6">2021</a>). Matching was done in <i>R</i> through the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>). The taxonomic standardization process was similar to the one completed <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">during the preparation of the third major release</a> of the <a href="https://apps.worldagroforestry.org/products/switchboard">Agroforestry Species Switchboard</a> and when preparing the <strong>GlobalUsefulNativeTrees database</strong> (GlobUNT; <a href="https://worldagroforestry.org/output/globalusefulnativetrees">https://worldagroforestry.org/output/globalusefulnativetrees</a>) .</p><p>Where a matching species was found in GlobUNT, the species name in the GlobUNT database has been shown. GlobUNT has been described in the following publication: Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) <strong>GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration</strong>. <i>Sci Rep</i> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a>.</p><p>The developments of this dataset and GlobUNT were supported by the Darwin Initiative to project DAREX001 of <a href="https://www.darwininitiative.org.uk/project/DAREX001/"><i>Developing a Global Biodiversity Standard certification for tree-planting and restoration</i></a> and by Norway's International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the <a href="https://www.worldagroforestry.org/project/provision-adequate-tree-seed-portfolio-ethiopia"><i>Provision of Adequate Tree Seed Portfolio</i></a> project in Ethiopia. </p>
Leaf samples of three common plant species collected in seven LandKlif quadrants
<p><span>Leaf samples of Acer pseudoplatanus, Dactylis glomerata and Potentilla reptans were collected in seven LandKlif quadrants along a climate gradient in summer 2020. In each quadrant, leaves were sampled in two habitats (forest and open landscape). In each habitat, three leaves from seven individuals of each species were collected. Specific leaf area (SLA) and leaf dry matter content (LDMC) of each leaf sample were determined in the lab. In addition, nitrogen content was measured at the level of individuals. This dataset contains the mean SLA and LDMC of the leaves sampled from each individual, as well as information on the site where they were collected.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Activity of antioxidant enzymes and lipid peroxidation of soybean plants treated with five Diaporthe species
<p>Absorbance data from spectrophotometric measurements of catalase, reduced glutathion, lipid peroxidation and superoxide-dismutase of soybean cv. Sava plants infected with five <em>Diaporthe</em> species (i.e. <em>D. aspalathi</em>, <em>D. caulivora</em>, <em>D. eres</em>, <em>D. gulyae</em>, <em>D. longicolla</em>).</p> <p>Supplementary data to the publication Petrovic et al. (2023) The biochemical response of soybean cultivars infected by <em>Diaporthe</em> species complex. Plants 12, 2896. https://doi.org/10.3390/plants12162896</p>
Phenology and production of pollen, nectar and sugar in 1612 plant species from various environments
<p>This dataset is related to the data paper published in Ecology: Filipiak et al, in press, <em>Phenology and production of pollen, nectar and sugar in 1612 plant species from various environments</em>; doi: to be provided when available. This paper must be always properly cited when using the database. Please use any scientific full citation format. <br> <br> <strong>Abstract</strong><br> To predict the quantity and quality of the food available for pollinators in various landscapes over time, it is necessary to collect detailed data on pollen, nectar, and sugar production per unit area and the flowering phenology of plants. Similar data are needed to estimate the contribution of plants to the functioning of food webs via the flow of energy and nutrients through the soil-plant-nectar/pollen-consumer pathway. Current knowledge on this topic is fragmented. This is the first database to compile data on the various food resources produced by 1612 different plant species, belonging to 755 genera and 133 families, including crops and wild plants, annuals and perennials, animal- and wind-pollinated plants, and weeds and trees growing in different ecosystems under various environmental conditions. The dataset consists of 103 parameters related to the traits of plant species, as well as to geographical and environmental factors, allowing for precise calculations of nectar, pollen and energy provided by plants and available to consumers in the considered flora or ecosystem on a daily basis throughout the year. These parameters, gathered by us and extracted from the available literature, describe pollen, nectar and sugar production (where applicable, in mass, volume and concentration units), honey yield, the timing and duration of flowering, flower longevity, numbers of plants and flowers per unit area, related weather conditions (temperature and precipitation), geographical location, landscape, and syntaxonomy. The data were obtained from various, mostly European, pedoclimatic zones, and the majority of the data were available for plant species and communities present in Central Europe, especially in Poland, where research on floral resources has a long tradition. These data are representative of the whole continent and may be used as a reference for plant communities occurring on continents other than Europe since the database allows the consideration of differences in the production of resources by a single plant species growing in different communities. This dataset provides a unique opportunity to test hypotheses related to the functioning of food webs, nutrient cycling, plant ecology, and pollinator ecology and conservation.</p>
Scanned images of monocultures and mixtures of six grassland plant species roots, and of simulated fine roots
<p>Soil core samples were taken from a multi-species grassland experiment with field plots of monocultures and mixtures of six grassland plant species: <em>Lolium perenne</em> L. (PRG),<em> Phleum pratense</em> L. (TIM), <em>Trifolium pratense</em> L. (RC), <em>Trifolium repens</em> L. (WC), <em>Cichorium intybus </em>L. (CHIC), and <em>Plantago lanceolata </em>L.. The multi-species plots had a two species mixture with <em>Trifolium repens </em>L. and<em> Lolium perenne</em> L. (PRGWC), and a 6 species mixture with all species mentioned above. The cores were separated into soil depths of 0-10 cm, 10-15 cm and 15-20 cm and the roots separated from the soil.</p> <p>A ground-truth image set was created to simulate fine roots using fishing line. The fishing line used was a clear copolymer monofilament (Greys<sup>TM</sup> Greylon Tippet Material 3 lb), measured using a scanning electron microscope (Hitachi SU8200) to be 0.14 mm in diameter. The fishing line was used in its clear colour or coloured black using a permanent marker to simulate unstained and stained fine roots respectively. The fishing line was cut into lengths of 30 cm or 5 cm. </p> <p>Roots and fishing line were scanned using an Epson Perfection V800 flatbed scanner at 600 dpi. </p> <p>The Roots ZIP file contains a folder for the scanned root images and the Line zip file contains a folder with the scanned fishing line. The excel spreadsheet describes the naming convention for the images.</p> <p>Further details about the root sampling and image acquisition can be found in the publication that analyses these images: <a href="https://doi.org/10.1002/ppj2.20034">https://doi.org/10.1002/ppj2.20034</a></p>
Genome-wide identification of cell-surface and intracellular immune receptors in 350 plant species
<p>Here we identified cell-surface (LRR-RLKs, LRR-RLPs, LysM-RLKs and LysM-RLPs) and intracellular immune receptors (NB-ARCs) from the genomes of 350 plant species. </p> <p> </p> <p>Zip file contains:</p> <p>Folder 'Immune_receptor_sequences' - FASTA files of the identified LRR-RLPs, Lys-RLKs, LysM-RLPs and NB-ARCs.</p> <p>Folder 'RLK_sequences' - FASTA files of the identified LRR-RLKs (all and 20 individual subgroups).</p> <p>Folder 'RLK_trees' - Phylogenetic TREE files of the identified LRR-RLKs (all and 20 individual subgroups); classified according to their kinase domains.</p> <p>238.species - Phylogenetic tree of the 238 plant species used in the analyses (taken from <a href="https://doi.org/10.1093/jpe/rtv047">https://doi.org/10.1093/jpe/rtv047</a>).</p> <p>350.species - Phylogenetic tree of the 350 plant species used in the analyses.</p> <p>simple.to.original.ids- Translator file for the original ID of each gene. </p> <p> </p>
Wood density for 26 plant species collected from Northern Western Ghats
<p>This dataset contains wood density estimates for species collected from Sindhudurg district of Maharashtra. The data was collected for baseline data generation for the Sahyadri Restoration Program as part of CEROS Lab at Nature Conservation Foundation. The fieldwork was carried out in Feb-Mar 2024.</p> <p>Wood cores were collected using Increment Borer (Haglof 12inch, 3 thread, 5.15mm)</p> <p>Usage notes:</p> <p>readme_wood_density.txt contains the information for each columns and the values calculated</p> <p>Wood Density Maharashtra.csv contains the dataset</p> <p><strong>ACKNOWLEDGEMENTS:</strong></p> <p>I would like to thank GCPL CSR (Godrej Consumers Products) for funding this data collection as part of the Sahyadri Restoration project and S.P.K College Sawantwadi for providing the necessary lab support. Special thanks to Dr. Deelip Bharmal (Principal S.P.K College) and Dr. G.S Margaj (Professor Zoology Dept) for help with the lab analysis.</p>
Projected distribution of invasive plant species in the tropical Andes under climate change
<p>Distribution maps of 11 invasive species now and in the future (2040-70). The projections were the result of the assembly of three algorithms: Adaptive Boosting (AdaBoost), Boosted Regression Trees (BRT), and Extreme Gradient Boosting (XGBoost). Future projections were made for three global circulation models and three climate change scenarios, each with low (SSP126), medium (SSP370), and high (SSP585) levels of carbon emission.</p> <p>Habitat suitability and presence/absence maps are also included. The threshold for establishing a species as present was determined to be the value that maximized the TSS. </p> <p>For more information, see the article accompanying the dataset by González-Trujillo et al. Mapping the threat: Projecting invasive plant distribution in the tropical Andes under climate change</p> <p>List of modeled invasive plant species and their known impacts in the tropics.</p> <table> <tbody> <tr> <td> <p><strong>Species </strong></p> </td> <td> <p><strong>Biogeographic origin</strong></p> </td> <td> <p><strong>Impacts </strong></p> </td> <td> <p><strong>References</strong></p> </td> <td> <p><strong>GBIF data (DOIs)</strong></p> </td> </tr> <tr> <td> <p><em>Acacia decurrens </em></p> </td> <td> <p>Australian</p> </td> <td> <p>Create regular layers of litter on the ground, inhibit or redirect successional processes, inhibit the expression of seed banks, and limit resource supply, leading to displacement of native plants and animals and increasing the frequency of fires.</p> </td> <td> <p> (Cárdenas López et al., 2017; Le Maitre et al., 2011)</p> </td> <td> <p>https://doi.org/10.15468/dl.mjyxhw</p> </td> </tr> <tr> <td> <p><em>Acacia melanoxylon</em></p> </td> <td> <p>Australian</p> </td> <td> <p>Alter the structure and function of their ecosystems, thereby displacing their native flora. It also causes soil erosion and alters hydrological cycles, negatively affecting agriculture.</p> </td> <td> <p>(Kumschick and Jansen, 2023; Le Maitre et al., 2011)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.4cugnk</p> </td> </tr> <tr> <td> <p><em>Arundo donax</em></p> <p><em> </em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter<em> </em>the natural vegetation structure, outcompete native plant species and diminish the diversity and abundance of animals such as arthropods and birds. It also drives out soil, fuels forest fires, displaces native species, and increases the invasion of ticks that affect livestock.</p> </td> <td> <p>(Cárdenas López et al., 2017; Girotto et al., 2021; Lambert et al., 2010)</p> </td> <td> <p>https://doi.org/10.15468/dl.bfep4t</p> </td> </tr> <tr> <td> <p><em>Genista monspessulana</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter fire regime and nutrient cycling displace native species and decrease native diversity by forming dense monospecific stands. It also facilitates the establishment of other invasive species and produces seeds that are toxic to livestock and humans.</p> </td> <td> <p>(Cárdenas López et al., 2017; Herrera et al., 2016; Pauchard et al., 2008)</p> </td> <td> <p>https://doi.org/10.15468/dl.gyhnxh</p> </td> </tr> <tr> <td> <p><em>Hedychium coronarium </em></p> </td> <td> <p>Indo-Malesian</p> </td> <td> <p>Alter hydrological and nutrient cycles in soil. It forms thickets that suppress the successional and regeneration processes of native species, thus affecting the native flora and crops.</p> </td> <td> <p>(Cárdenas López et al., 2017; Costa et al., 2019)</p> </td> <td> <p>https://doi.org/10.15468/dl.6z2jgb</p> </td> </tr> <tr> <td> <p><em>Melinis minutiflora</em></p> </td> <td> <p>African</p> </td> <td> <p>Increases the occurrence of fires, displaces native species, and alters soil properties and decomposition. It also inhibits the growth of native species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Nogueira et al., 2019; Sandoval et al., 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.fsqwsv</p> </td> </tr> <tr> <td> <p><em>Pteridium aquilinum</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p> (Berget et al., 2015; Cárdenas López et al., 2017; Valdez-Ramírez et al., 2020)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.sp4uuv</p> </td> </tr> <tr> <td> <p><em>Ricinus communis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Sandoval et al., 2022; Silva and Fabricante, 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.dhbphb</p> </td> </tr> <tr> <td> <p><em>Senecio madagascariensis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter soil nutrient cycles, damage to agricultural crops, and outcompete native species. It also contains substances that are toxic to both animals and humans. </p> </td> <td> <p>(Wijayabandara et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.7e8eyx</p> </td> </tr> <tr> <td> <p><em>Thunbergia alata</em></p> </td> <td> <p>African</p> </td> <td> <p>Displace native species and reduce habitat heterogeneity, thereby affecting the structure and function of native ecosystems.</p> </td> <td> <p>(Cárdenas López et al., 2017; Quijano-Abril et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.g9zybc</p> </td> </tr> <tr> <td> <p><em>Ulex europeaus</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Dry soil and increase the occurrence of fires. Inhibits vegetative growth, including pastures in agricultural and livestock lands.</p> </td> <td> <p>(Anderson and Anderson, 2009; Cárdenas López et al., 2017)</p> </td> <td> <p>https://doi.org/10.15468/dl.6642q9</p> </td> </tr> </tbody> </table>
GO Term annotations for five plants species from Phytozome by FANTASIA
<p>This is the GO term annotation made with FANTASIA for five species (Arabidopsis thaliana, Oryza sativa, Zea mays, Populus trichocarpa, and Solanum lycopersicum) from the Phytozome 13 datasets as proof of concept for this tool.</p>
Data from the National Prioritisation of Australian plant species after the 2019-2020 bushfires
<p>Data for 26,062 native Australian plant species assessed against ten post-fire recovery criteria. Details of criteria and methods available in Gallagher, R. V. (2020) <em>National prioritisation of Australian plants affected by the 2019–2020 bushfire season.</em> Report to the Commonwealth Dartement of Agriculture, Water and Environment. https://www.environment.gov.au/system/files/pages/289205b6-83c5-480c-9a7d-3fdf3cde2f68/files/final-national-prioritisation-australian-plants-affected-2019-2020-bushfire-season.pdf </p>
Tea bag index (TBI) for a pot trial with five plant species in Trani (Apulia, Italy)
<p>The file contains mean k and S value per GPS location, with as meta-data the starting date, duration and biome of the study.</p>
DCA and GNMDS output for 4640 subplots and 95 vascular plant species in four alpine grasslands
<p>Ordination output from detrended correspondence analysis (DCA) and global non-metric multidimensional scaling (GNMDS).</p> <p>Analyses were performed in R with the <em>vegan</em> package (Oksanen 2022) for the entire data set of 4630 subplots and 95 species' occurrences ('global', indicated by global or missing site name in file names), and for each of four sites: Skjellingahaugen (skj), Gudmedalen (gud), Låvisdalen (lav), and Ulvehaugen (ulv). Access .Rds files with readRDS in R/RStudio.</p> <p>For GNMDS files, k indicates the chosen number of dimensions. See GitHub repository for scripts to produce and perform further analysis with the files in this archive.</p> <p>Analyses performed by EL with scripts based on originals by RH.</p>
Dataset Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial Pseudomonas in the wheat rhizosphere
<p>This dataset is related to the paper "<strong>Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial <em>Pseudomonas </em>in the wheat rhizosphere</strong>" (Garrido-Sanz et al., 2023, doi: 10.1186/s40168-023-01660-5) and contains the data obtained from bacterial competition asays and plant-growth measurements.</p> <p>Sequencing data used in this study has been deposited in the NCBI Sequence Read Archive (RSA) under the BioProject accession number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA948847">PRJNA948847</a>.</p> <p>The R script used to analyze the data generated in the paper is available at <a href="https://github.com/dgarrs/Pprotegens_proliferation_NatComs">GitHub </a>and <a href="https://doi.org/10.5281/zenodo.8322086">Zenodo</a>.</p>
Data for “Herbivory damage but not plant disease under experimental warming is dependent on weather for three subalpine grass species”, Rocky Mountain Biological Laboratory, Gothic, Colorado, 2015-2017.
Both theory and prior studies predict that climate warming should increase attack rates by herbivores and pathogens on plants. However, past work has often assumed that variation in abiotic conditions other than temperature (e.g., precipitation) do not alter warming responses of plant damage by natural enemies. Studies over short time periods span low variation in weather, and studies over long-time scales often neglect to account for fine-scale weather conditions. Here, we used a 20+ year field warming experiment to investigate if warming affects herbivory and disease are dependent on variation in ambient weather observed over three years. We studied three common grass species in a subalpine meadow in the Colorado Rocky Mountains, USA. We visually estimated herbivory and disease every two-weeks during the growing season and evaluated weather conditions during the previous two- or four-week time interval (two-week average air temperature, two- and four-week cumulative precipitation) as predictors of the probability and amount of damage. Herbivore attack was 13% more likely and amount of damage was 29% greater in warmed plots than controls across the focal species, but warming treatment had little affect on plant disease. Herbivory presence and damage increased the most with experimental warming when preceded by wetter, rather than drier, fine-scale weather, but preceding ambient temperature did not strongly interact with elevated warming to influence herbivory. Disease presence and damage increased, on average, with warmer weather and more precipitation regardless of warming. The effect of warming over reference climate on herbivore damage is dependent on and amplified by fine-scale weather variation, suggesting more boom-and-bust damage dynamics with increasing climate variability. However, the mean effect of regional climate change is likely reduced monsoon rainfall, for which we predict a reduction in insect herbivore damage. Plant disease was generally unrelated
Projected climate and canopy change lead to thermophilization and homogenization of forest floor vegetation in a hotspot of plant species richness, Berchtesgaden National Park, Bavaria, Germany
Mountain forests are plant diversity hotspots, but changing climate and increasing forest disturbances will likely lead to far-reaching plant community change. Projecting future change, however, is challenging for forest understory plants, which respond to forest structure and composition as well as climate. Here, we jointly assessed effects of both climate and forest change, including wind and bark beetle disturbances, using the process-based simulation model iLand in a protected landscape in the northern Alps (Berchtesgaden National Park, Germany), asking: (1) How do understory plant communities respond to 21st-century change in a topographically complex mountain landscape, representing a hotspot of plant species richness? (2) How important are climatic changes (i.e., direct climate effects) versus forest structure and composition changes (i.e., indirect climate effects and recovery from past land use) in driving understory responses at landscape scales? Stacked individual species distribution models fit with climate, forest, and soil predictors (248 species currently present in the landscape, derived from 150 field plots stratified by elevation and forest development, overall AUC = 0.86) were driven with projected climate (RCP4.5 and RCP8.5) and modeled forest variables to predict plant community change. Nearly all species persisted in the landscape in 2050, but on average 8% of the species pool was lost by the end of the century. By 2100, landscape mean species richness and understory cover declined (-13% and -8%, respectively), warm-adapted species increasingly dominated plant communities (i.e., thermophilization, +12%), and plot-level turnover was high (62%). Subalpine forests experienced the greatest richness declines (-16%), most thermophilization (+17%), and highest turnover (67%), resulting in plant community homogenization across elevation zones. Climate rather than forest change was the dominant driver of understory responses. The magnitude of unabated 2
Combined data on plant species abundance and composition from LTER and other grasslands in the United States, 1943 - 2010
Understanding how biotic mechanisms confer stability in variable environments is a fundamental quest in ecology, and one that is becoming increasingly urgent with global change. Several mechanisms, notably a portfolio effect associated with species richness, compensatory dynamics generated by negative species covariance and selection for stable dominant species populations can increase the stability of the overall community. While the importance of these mechanisms is debated, few studies have contrasted their importance in an environmental context. We compiled nine long-term datasets of grassland species composition to evaluate the strength of biotic mechanisms of community stability and assess how these mechanisms change across precipitation gradients. Data were collected in replicate plots over time at nine different sites throughout the US (replicates within a site range from 5-100, plot sizes from 0.1 m² to 17 m²; minimum 9 years, maximum 30 years). Species abundance was measured as either percent cover, biomass, or allometrically-derived biomass. At all sites the measurement techniques and management regimes remained constant over the collection period, and the data collection methods were not relativized. For example, sites in which species composition were measured as percent cover do not require their estimates to sum to 100. For sites with long-term experimental treatments, only the control plots were included. These data have been presented in: Lauren M. Hallett, Joanna S. Hsu, Elsa E. Cleland, Scott L. Collins, Timothy L. Dickson, Emily C. Farrer, Laureano A. Gherardi, Katherine L. Gross, Richard J. Hobbs, Laura Turnbull, Katharine N. Suding. 2014. Biotic mechanisms of community stability shift along a precipitation gradient. Ecology 95:1693–1700. http://dx.doi.org/10.1890/13-0895.1
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.